Improvements of AIMS D2DB matching for product patterns

Masaharu Nishiguchi; Koichi Kanno; Hiroyuki Miyashita; Kana Ohara; Donghwan Son; Vikram Tolani; Masaki Satake

doi:10.1117/12.2197609

9 July 2015 Improvements of AIMS D2DB matching for product patterns

Masaharu Nishiguchi, Koichi Kanno, Hiroyuki Miyashita, Kana Ohara, Donghwan Son, Vikram Tolani, Masaki Satake

Proceedings Volume 9658, Photomask Japan 2015: Photomask and Next-Generation Lithography Mask Technology XXII; 96580W (2015) https://doi.org/10.1117/12.2197609
Event: Photomask Japan 2015, 2015, Yokohama, Japan

Abstract

AIMS^TM is mainly used in photomask industry for verifying the print impact of mask defects on wafer CD in DUV lithography process. AIMS verification is typically used in D2D configuration, wherein two AIMS images, reference and defect, are captured and compared. Criticality of defects is then analyzed off these images using a number of criteria. As photomasks with aggressive OPC, sub-resolution assist features (SRAFs), and single-die are being routinely manufactured in production environment, it is required to improve cycle time through the AIMS step by saving time in searching for and capturing an adequate reference AIMS image. One solution is to use AIMS D2DB methodology which compares AIMS defect image with a reference image simulated from the corresponding mask design data. In general, such simulation needs calibration with the native images captured on the AIMS tool. In our previous paper we evaluated a calibration procedure directly using the defect AIMS image and compared the analysis results with a D2D capture using AIA (Aerial Image Analyzer) software product from Luminescent Technologies (now part of KLA-Tencor Corporation). The results showed that calibration using defect AIMS image does not influence AIMS judgment as long as the defect size is less than 100nm in case of typical basic patterns. When applying this methodology to product patterns, it was found that there were differences between reference AIMS image and simulation image. These differences influenced AIMS verification. Then new method to compensate would be needed. Our approach to compensate the difference between AIMS image and simulated image is examination with some factors likely to cause the difference.

Citation Download Citation

Masaharu Nishiguchi, Koichi Kanno, Hiroyuki Miyashita, Kana Ohara, Donghwan Son, Vikram Tolani, and Masaki Satake "Improvements of AIMS D2DB matching for product patterns", Proc. SPIE 9658, Photomask Japan 2015: Photomask and Next-Generation Lithography Mask Technology XXII, 96580W (9 July 2015); https://doi.org/10.1117/12.2197609

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KEYWORDS

Photomasks

Calibration

SRAF

Optical proximity correction

Deep ultraviolet

Image analysis

Lithography

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